Overview

Dataset statistics

Number of variables10
Number of observations214
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory16.8 KiB
Average record size in memory80.6 B

Variable types

Numeric10

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
RI is highly correlated with Na and 5 other fieldsHigh correlation
Na is highly correlated with RI and 5 other fieldsHigh correlation
Mg is highly correlated with Na and 4 other fieldsHigh correlation
Al is highly correlated with RI and 7 other fieldsHigh correlation
Si is highly correlated with RI and 5 other fieldsHigh correlation
K is highly correlated with RI and 3 other fieldsHigh correlation
Ca is highly correlated with RI and 7 other fieldsHigh correlation
Ba is highly correlated with RI and 3 other fieldsHigh correlation
Type is highly correlated with Na and 4 other fieldsHigh correlation
Mg has 42 (19.6%) zeros Zeros
K has 30 (14.0%) zeros Zeros
Ba has 176 (82.2%) zeros Zeros
Fe has 144 (67.3%) zeros Zeros

Reproduction

Analysis started2022-12-04 06:55:05.535162
Analysis finished2022-12-04 06:55:16.476835
Duration10.94 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

RI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct178
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.518365421
Minimum1.51115
Maximum1.53393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:16.610685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.51115
5-th percentile1.515401
Q11.5165225
median1.51768
Q31.5191575
95-th percentile1.523664
Maximum1.53393
Range0.02278
Interquartile range (IQR)0.002635

Descriptive statistics

Standard deviation0.003036863739
Coefficient of variation (CV)0.002000087527
Kurtosis4.931737386
Mean1.518365421
Median Absolute Deviation (MAD)0.001265
Skewness1.625430506
Sum324.9302
Variance9.222541372 × 10-6
MonotonicityNot monotonic
2022-12-04T12:25:16.699454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.521523
 
1.4%
1.51593
 
1.4%
1.516453
 
1.4%
1.517542
 
0.9%
1.518412
 
0.9%
1.516742
 
0.9%
1.517682
 
0.9%
1.516552
 
0.9%
1.518112
 
0.9%
1.522132
 
0.9%
Other values (168)191
89.3%
ValueCountFrequency (%)
1.511151
0.5%
1.511311
0.5%
1.512151
0.5%
1.512991
0.5%
1.513161
0.5%
1.513211
0.5%
1.514091
0.5%
1.515081
0.5%
1.515142
0.9%
1.515311
0.5%
ValueCountFrequency (%)
1.533931
0.5%
1.531251
0.5%
1.527771
0.5%
1.527391
0.5%
1.527251
0.5%
1.526671
0.5%
1.526641
0.5%
1.526141
0.5%
1.524751
0.5%
1.52411
0.5%

Na
Real number (ℝ≥0)

HIGH CORRELATION

Distinct142
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.40785047
Minimum10.73
Maximum17.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:16.790312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10.73
5-th percentile12.415
Q112.9075
median13.3
Q313.825
95-th percentile14.8535
Maximum17.38
Range6.65
Interquartile range (IQR)0.9175

Descriptive statistics

Standard deviation0.8166035557
Coefficient of variation (CV)0.06090488238
Kurtosis3.052232409
Mean13.40785047
Median Absolute Deviation (MAD)0.435
Skewness0.4541814537
Sum2869.28
Variance0.6668413672
MonotonicityNot monotonic
2022-12-04T12:25:16.871081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.025
 
2.3%
13.215
 
2.3%
135
 
2.3%
13.644
 
1.9%
13.334
 
1.9%
13.244
 
1.9%
12.854
 
1.9%
12.864
 
1.9%
12.933
 
1.4%
13.413
 
1.4%
Other values (132)173
80.8%
ValueCountFrequency (%)
10.731
0.5%
11.021
0.5%
11.031
0.5%
11.231
0.5%
11.451
0.5%
11.561
0.5%
11.951
0.5%
12.161
0.5%
12.21
0.5%
12.31
0.5%
ValueCountFrequency (%)
17.381
0.5%
15.791
0.5%
15.151
0.5%
15.011
0.5%
14.991
0.5%
14.952
0.9%
14.941
0.5%
14.921
0.5%
14.862
0.9%
14.852
0.9%

Mg
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct94
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.68453271
Minimum0
Maximum4.49
Zeros42
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:16.956106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.115
median3.48
Q33.6
95-th percentile3.85
Maximum4.49
Range4.49
Interquartile range (IQR)1.485

Descriptive statistics

Standard deviation1.442407845
Coefficient of variation (CV)0.5373031364
Kurtosis-0.4103189629
Mean2.68453271
Median Absolute Deviation (MAD)0.205
Skewness-1.152559318
Sum574.49
Variance2.080540391
MonotonicityNot monotonic
2022-12-04T12:25:17.053355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042
 
19.6%
3.488
 
3.7%
3.588
 
3.7%
3.548
 
3.7%
3.527
 
3.3%
3.625
 
2.3%
3.574
 
1.9%
3.614
 
1.9%
3.664
 
1.9%
3.54
 
1.9%
Other values (84)120
56.1%
ValueCountFrequency (%)
042
19.6%
0.331
 
0.5%
0.781
 
0.5%
1.011
 
0.5%
1.351
 
0.5%
1.611
 
0.5%
1.711
 
0.5%
1.741
 
0.5%
1.781
 
0.5%
1.831
 
0.5%
ValueCountFrequency (%)
4.491
 
0.5%
3.981
 
0.5%
3.971
 
0.5%
3.931
 
0.5%
3.93
1.4%
3.891
 
0.5%
3.871
 
0.5%
3.861
 
0.5%
3.852
0.9%
3.841
 
0.5%

Al
Real number (ℝ≥0)

HIGH CORRELATION

Distinct118
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.444906542
Minimum0.29
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:17.159240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.696
Q11.19
median1.36
Q31.63
95-th percentile2.394
Maximum3.5
Range3.21
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.4992696456
Coefficient of variation (CV)0.3455376739
Kurtosis2.060568969
Mean1.444906542
Median Absolute Deviation (MAD)0.21
Skewness0.907289809
Sum309.21
Variance0.249270179
MonotonicityNot monotonic
2022-12-04T12:25:17.253395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.548
 
3.7%
1.196
 
2.8%
1.435
 
2.3%
1.295
 
2.3%
1.235
 
2.3%
1.565
 
2.3%
1.364
 
1.9%
1.354
 
1.9%
1.284
 
1.9%
1.253
 
1.4%
Other values (108)165
77.1%
ValueCountFrequency (%)
0.291
0.5%
0.341
0.5%
0.472
0.9%
0.511
0.5%
0.562
0.9%
0.581
0.5%
0.651
0.5%
0.661
0.5%
0.671
0.5%
0.711
0.5%
ValueCountFrequency (%)
3.51
0.5%
3.041
0.5%
3.021
0.5%
2.881
0.5%
2.791
0.5%
2.741
0.5%
2.681
0.5%
2.661
0.5%
2.541
0.5%
2.511
0.5%

Si
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.65093458
Minimum69.81
Maximum75.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:17.344311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum69.81
5-th percentile71.315
Q172.28
median72.79
Q373.0875
95-th percentile73.5175
Maximum75.41
Range5.6
Interquartile range (IQR)0.8075

Descriptive statistics

Standard deviation0.7745457948
Coefficient of variation (CV)0.0106611952
Kurtosis2.967902956
Mean72.65093458
Median Absolute Deviation (MAD)0.385
Skewness-0.7304472251
Sum15547.3
Variance0.5999211882
MonotonicityNot monotonic
2022-12-04T12:25:17.432576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.864
 
1.9%
72.994
 
1.9%
73.14
 
1.9%
73.284
 
1.9%
73.114
 
1.9%
71.993
 
1.4%
73.393
 
1.4%
72.643
 
1.4%
72.953
 
1.4%
72.853
 
1.4%
Other values (123)179
83.6%
ValueCountFrequency (%)
69.811
0.5%
69.891
0.5%
70.161
0.5%
70.261
0.5%
70.431
0.5%
70.481
0.5%
70.571
0.5%
70.71
0.5%
71.151
0.5%
71.241
0.5%
ValueCountFrequency (%)
75.411
0.5%
75.181
0.5%
74.551
0.5%
74.451
0.5%
73.881
0.5%
73.811
0.5%
73.751
0.5%
73.721
0.5%
73.71
0.5%
73.611
0.5%

K
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4970560748
Minimum0
Maximum6.21
Zeros30
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:17.523226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1225
median0.555
Q30.61
95-th percentile0.76
Maximum6.21
Range6.21
Interquartile range (IQR)0.4875

Descriptive statistics

Standard deviation0.6521918456
Coefficient of variation (CV)1.312109194
Kurtosis54.68969853
Mean0.4970560748
Median Absolute Deviation (MAD)0.115
Skewness6.55164831
Sum106.37
Variance0.4253542034
MonotonicityNot monotonic
2022-12-04T12:25:17.601450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030
 
14.0%
0.5712
 
5.6%
0.5611
 
5.1%
0.611
 
5.1%
0.5810
 
4.7%
0.648
 
3.7%
0.618
 
3.7%
0.597
 
3.3%
0.556
 
2.8%
0.546
 
2.8%
Other values (55)105
49.1%
ValueCountFrequency (%)
030
14.0%
0.021
 
0.5%
0.031
 
0.5%
0.042
 
0.9%
0.051
 
0.5%
0.064
 
1.9%
0.071
 
0.5%
0.084
 
1.9%
0.092
 
0.9%
0.11
 
0.5%
ValueCountFrequency (%)
6.212
0.9%
2.71
0.5%
1.761
0.5%
1.681
0.5%
1.461
0.5%
1.411
0.5%
1.11
0.5%
0.971
0.5%
0.811
0.5%
0.762
0.9%

Ca
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.956962617
Minimum5.43
Maximum16.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:17.682440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.43
5-th percentile7.8125
Q18.24
median8.6
Q39.1725
95-th percentile11.5615
Maximum16.19
Range10.76
Interquartile range (IQR)0.9325

Descriptive statistics

Standard deviation1.423153487
Coefficient of variation (CV)0.1588879566
Kurtosis6.681977951
Mean8.956962617
Median Absolute Deviation (MAD)0.445
Skewness2.047053913
Sum1916.79
Variance2.025365848
MonotonicityNot monotonic
2022-12-04T12:25:17.759510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.035
 
2.3%
8.435
 
2.3%
9.574
 
1.9%
8.794
 
1.9%
8.444
 
1.9%
8.63
 
1.4%
8.393
 
1.4%
8.553
 
1.4%
8.673
 
1.4%
9.853
 
1.4%
Other values (133)177
82.7%
ValueCountFrequency (%)
5.431
0.5%
5.791
0.5%
5.871
0.5%
6.471
0.5%
6.651
0.5%
6.931
0.5%
6.961
0.5%
7.081
0.5%
7.361
0.5%
7.591
0.5%
ValueCountFrequency (%)
16.191
0.5%
14.961
0.5%
14.681
0.5%
14.41
0.5%
13.441
0.5%
13.31
0.5%
13.241
0.5%
12.51
0.5%
12.241
0.5%
11.641
0.5%

Ba
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.175046729
Minimum0
Maximum3.15
Zeros176
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:17.993749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.57
Maximum3.15
Range3.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4972192606
Coefficient of variation (CV)2.840494441
Kurtosis12.54108358
Mean0.175046729
Median Absolute Deviation (MAD)0
Skewness3.416424569
Sum37.46
Variance0.2472269931
MonotonicityNot monotonic
2022-12-04T12:25:18.068552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0176
82.2%
0.642
 
0.9%
1.572
 
0.9%
0.092
 
0.9%
1.592
 
0.9%
0.112
 
0.9%
3.151
 
0.5%
0.811
 
0.5%
1.641
 
0.5%
1.061
 
0.5%
Other values (24)24
 
11.2%
ValueCountFrequency (%)
0176
82.2%
0.061
 
0.5%
0.092
 
0.9%
0.112
 
0.9%
0.141
 
0.5%
0.151
 
0.5%
0.241
 
0.5%
0.271
 
0.5%
0.41
 
0.5%
0.531
 
0.5%
ValueCountFrequency (%)
3.151
0.5%
2.881
0.5%
2.21
0.5%
1.711
0.5%
1.681
0.5%
1.671
0.5%
1.641
0.5%
1.631
0.5%
1.592
0.9%
1.572
0.9%

Fe
Real number (ℝ≥0)

ZEROS

Distinct32
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05700934579
Minimum0
Maximum0.51
Zeros144
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:18.137677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.267
Maximum0.51
Range0.51
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.09743870064
Coefficient of variation (CV)1.709170651
Kurtosis2.662015617
Mean0.05700934579
Median Absolute Deviation (MAD)0
Skewness1.75432747
Sum12.2
Variance0.009494300382
MonotonicityNot monotonic
2022-12-04T12:25:18.201170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0144
67.3%
0.247
 
3.3%
0.177
 
3.3%
0.096
 
2.8%
0.15
 
2.3%
0.114
 
1.9%
0.163
 
1.4%
0.283
 
1.4%
0.123
 
1.4%
0.223
 
1.4%
Other values (22)29
 
13.6%
ValueCountFrequency (%)
0144
67.3%
0.011
 
0.5%
0.031
 
0.5%
0.051
 
0.5%
0.061
 
0.5%
0.073
 
1.4%
0.082
 
0.9%
0.096
 
2.8%
0.15
 
2.3%
0.114
 
1.9%
ValueCountFrequency (%)
0.511
 
0.5%
0.371
 
0.5%
0.351
 
0.5%
0.341
 
0.5%
0.321
 
0.5%
0.311
 
0.5%
0.31
 
0.5%
0.291
 
0.5%
0.283
1.4%
0.261
 
0.5%

Type
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.780373832
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2022-12-04T12:25:18.256930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.103738646
Coefficient of variation (CV)0.7566387736
Kurtosis-0.2795182977
Mean2.780373832
Median Absolute Deviation (MAD)1
Skewness1.114915201
Sum595
Variance4.425716292
MonotonicityIncreasing
2022-12-04T12:25:18.298059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
276
35.5%
170
32.7%
729
 
13.6%
317
 
7.9%
513
 
6.1%
69
 
4.2%
ValueCountFrequency (%)
170
32.7%
276
35.5%
317
 
7.9%
513
 
6.1%
69
 
4.2%
729
 
13.6%
ValueCountFrequency (%)
729
 
13.6%
69
 
4.2%
513
 
6.1%
317
 
7.9%
276
35.5%
170
32.7%

Interactions

2022-12-04T12:25:15.548556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.147642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.891739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.635839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.277317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.026868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.662852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.420036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.080789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.752406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.613613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.294950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.039561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.698613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.343322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.092671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.728556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.485456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.150362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.818968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.674314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.361256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.106267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.763467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.495430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.157853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.792036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.552058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.218528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.886570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.737591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.425171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.171680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.822753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.556737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.219646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.855706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.615725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.286791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.946572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.803933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.492510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.238244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.888082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.622775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.283042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.923358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.685734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.351968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.017082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.869192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.557188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.303592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.952397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.694236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.347255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.990073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.751171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.418721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.098727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.934439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.621938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.369799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.017543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.759053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.407087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.060014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.814789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.483374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.165041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:16.000526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.689000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.436486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.082274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.824160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.469352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.123158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.879234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.550698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.230590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:16.066381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.759517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.502333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.149654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.892927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.535511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.287956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.944743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.618489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.298982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:16.135832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:09.825450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:10.570044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.210445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:11.959049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:12.599210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:13.354217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.016569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:14.684155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-04T12:25:15.364494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-04T12:25:18.352836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-04T12:25:18.464777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-04T12:25:18.547568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-04T12:25:18.634456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-04T12:25:16.248449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-04T12:25:16.332562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

RINaMgAlSiKCaBaFeType
01.5210113.640004.490001.1000071.780000.060008.750000.000000.000001
11.5176113.890003.600001.3600072.730000.480007.830000.000000.000001
21.5161813.530003.550001.5400072.990000.390007.780000.000000.000001
31.5176613.210003.690001.2900072.610000.570008.220000.000000.000001
41.5174213.270003.620001.2400073.080000.550008.070000.000000.000001
51.5159612.790003.610001.6200072.970000.640008.070000.000000.260001
61.5174313.300003.600001.1400073.090000.580008.170000.000000.000001
71.5175613.150003.610001.0500073.240000.570008.240000.000000.000001
81.5191814.040003.580001.3700072.080000.560008.300000.000000.000001
91.5175513.000003.600001.3600072.990000.570008.400000.000000.110001

Last rows

RINaMgAlSiKCaBaFeType
2041.5161714.950000.000002.2700073.300000.000008.710000.670000.000007
2051.5173214.950000.000001.8000072.990000.000008.610001.550000.000007
2061.5164514.940000.000001.8700073.110000.000008.670001.380000.000007
2071.5183114.390000.000001.8200072.860001.410006.470002.880000.000007
2081.5164014.370000.000002.7400072.850000.000009.450000.540000.000007
2091.5162314.140000.000002.8800072.610000.080009.180001.060000.000007
2101.5168514.920000.000001.9900073.060000.000008.400001.590000.000007
2111.5206514.360000.000002.0200073.420000.000008.440001.640000.000007
2121.5165114.380000.000001.9400073.610000.000008.480001.570000.000007
2131.5171114.230000.000002.0800073.360000.000008.620001.670000.000007

Duplicate rows

Most frequently occurring

RINaMgAlSiKCaBaFeType# duplicates
01.5221314.210003.820000.4700071.770000.110009.570000.000000.0000012